Descriptif
This course aims at providing the bases of symbolic AI, along with a few selected advanced topics.
It includes courses on formal logics, ontologies, symbolic learning, typical AI topics such as revision, merging, etc., with illustrations on preference modeling and image understanding.
Objectifs pédagogiques
At the end of the course students will be able to understand different kinds of logic families, formulate reasoning in such formal languages, and manioulate tools to represent knowledge and its adaptation to imprecise and incomplete domains through the use of OWL and Protegé.
Diplôme(s) concerné(s)
- Echange international non diplomant
- Parallel & Distributed Systems M2
- Programme de mobilité des établissements français partenaires
- Diplôme d'ingénieur
- Data & Artificial Intelligence
- IA : Intelligence Artificielle
Parcours de rattachement
Pour les étudiants du diplôme Echange international non diplomant
Basic knowledge in computer sciences and algebra.
Pour les étudiants du diplôme Programme de mobilité des établissements français partenaires
Basic knowledge in computer sciences and algebra.
Pour les étudiants du diplôme Diplôme d'ingénieur
Basic knowledge in computer sciences and algebra.
Pour les étudiants du diplôme Data & Artificial Intelligence
Basic knowledge in computer sciences and algebra.
Pour les étudiants du diplôme IA : Intelligence Artificielle
Basic knowledge in computer sciences and algebra.
Format des notes
Numérique sur 20Littérale/grade européenPour les étudiants du diplôme Parallel & Distributed Systems M2
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 2.5 ECTS
Pour les étudiants du diplôme Echange international non diplomant
Vos modalités d'acquisition :
The course will be evaluated based on a written exam (50%) and the reports handed in after the practical work, which will require to create a couple of ontologies as part of a decision support system of a freely elected domain problem (50%).
- Crédits ECTS acquis : 2 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme IA : Intelligence Artificielle
Vos modalités d'acquisition :
The course will be evaluated based on a written exam (50%) and the reports handed in after the practical work, which will require to create a couple of ontologies as part of a decision support system of a freely elected domain problem (50%).
Le rattrapage est autorisé (Max entre les deux notes)- le rattrapage est obligatoire si :
- Note initiale < 6
- le rattrapage peut être demandé par l'étudiant si :
- 6 ≤ note initiale < 10
- Crédits ECTS acquis : 2 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Programme de mobilité des établissements français partenaires
Vos modalités d'acquisition :
The course will be evaluated based on a written exam (50%) and the reports handed in after the practical work, which will require to create a couple of ontologies as part of a decision support system of a freely elected domain problem (50%).
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 2 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Diplôme d'ingénieur
Vos modalités d'acquisition :
The course will be evaluated based on a written exam (50%) and the reports handed in after the practical work, which will require to create a couple of ontologies as part of a decision support system of a freely elected domain problem (50%).
- Crédits ECTS acquis : 2 ECTS
- Crédit d'Option 3A acquis : 2
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Data & Artificial Intelligence
Vos modalités d'acquisition :
The course will be evaluated based on a written exam (50%) and the reports handed in after the practical work, which will require to create a couple of ontologies as part of a decision support system of a freely elected domain problem (50%).
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 2.5 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Programme détaillé
Introduction - Reminder on logics (syntax, semantics...) and overview of several logics (propositional, first order, modal...)
Description logics, fuzzy logics, ontologies and Knowledge Graphs
Symbolic learning: formal concept analysis, decision trees
Tutorial on ontology engineering and design. Building your own ontologies using OWL and Protegé for real life knowledge graph problems- (practical work, including a report at the end of the course)
Some typical examples in AI: revision, merging, abduction, with illustrations on preference modeling and image understanding
